Overview

Dataset statistics

Number of variables18
Number of observations9785
Missing cells1721
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 MiB
Average record size in memory1.6 KiB

Variable types

Text14
Categorical2
Numeric1
DateTime1

Alerts

Bedrooms_df1 is highly imbalanced (51.8%)Imbalance
Bathrooms_df1 is highly imbalanced (51.6%)Imbalance
Price_df2 has 144 (1.5%) missing valuesMissing
Area_df2 has 151 (1.5%) missing valuesMissing
Bedrooms_df2 has 149 (1.5%) missing valuesMissing
Bathrooms_df2 has 151 (1.5%) missing valuesMissing
Floors has 155 (1.6%) missing valuesMissing
Amenities has 174 (1.8%) missing valuesMissing
Street name has 152 (1.6%) missing valuesMissing
Ward name has 170 (1.7%) missing valuesMissing
District name has 149 (1.5%) missing valuesMissing
Frontages has 175 (1.8%) missing valuesMissing
Main road has 151 (1.5%) missing valuesMissing
Listing ID has unique valuesUnique

Reproduction

Analysis started2024-06-21 08:42:22.579853
Analysis finished2024-06-21 08:42:28.123804
Duration5.54 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct1074
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size1004.3 KiB
2024-06-21T15:42:28.644852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.6399591
Min length4

Characters and Unicode

Total characters64972
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique493 ?
Unique (%)5.0%

Sample

1st row3899000000 tỷ
2nd rowThỏa thuận
3rd row12.8 tỷ
4th row3.4 tỷ
5th rowThỏa thuận
ValueCountFrequency (%)
tỷ 8760
44.8%
thỏa 849
 
4.3%
thuận 849
 
4.3%
5.5 176
 
0.9%
triệu 172
 
0.9%
4.5 164
 
0.8%
6.5 153
 
0.8%
5 149
 
0.8%
6 139
 
0.7%
5.8 127
 
0.6%
Other values (1026) 8032
41.0%
2024-06-21T15:42:29.352261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9785
15.1%
t 9781
15.1%
8760
13.5%
. 6556
10.1%
5 4115
 
6.3%
0 3240
 
5.0%
3 2213
 
3.4%
4 2173
 
3.3%
1 2171
 
3.3%
6 1922
 
3.0%
Other values (16) 14256
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9785
15.1%
t 9781
15.1%
8760
13.5%
. 6556
10.1%
5 4115
 
6.3%
0 3240
 
5.0%
3 2213
 
3.4%
4 2173
 
3.3%
1 2171
 
3.3%
6 1922
 
3.0%
Other values (16) 14256
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9785
15.1%
t 9781
15.1%
8760
13.5%
. 6556
10.1%
5 4115
 
6.3%
0 3240
 
5.0%
3 2213
 
3.4%
4 2173
 
3.3%
1 2171
 
3.3%
6 1922
 
3.0%
Other values (16) 14256
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9785
15.1%
t 9781
15.1%
8760
13.5%
. 6556
10.1%
5 4115
 
6.3%
0 3240
 
5.0%
3 2213
 
3.4%
4 2173
 
3.3%
1 2171
 
3.3%
6 1922
 
3.0%
Other values (16) 14256
21.9%
Distinct501
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size587.9 KiB
2024-06-21T15:42:29.892249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.5121104
Min length3

Characters and Unicode

Total characters44151
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)2.2%

Sample

1st row150 m
2nd rowNo Area
3rd row75 m
4th row110 m
5th row12 m
ValueCountFrequency (%)
m 8780
44.9%
no 1005
 
5.1%
area 1005
 
5.1%
60 468
 
2.4%
40 329
 
1.7%
80 326
 
1.7%
48 289
 
1.5%
50 281
 
1.4%
52 245
 
1.3%
45 218
 
1.1%
Other values (493) 6624
33.8%
2024-06-21T15:42:30.571496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9785
22.2%
m 8780
19.9%
0 3003
 
6.8%
5 2726
 
6.2%
4 2477
 
5.6%
6 2175
 
4.9%
1 1844
 
4.2%
2 1789
 
4.1%
8 1733
 
3.9%
3 1639
 
3.7%
Other values (9) 8200
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9785
22.2%
m 8780
19.9%
0 3003
 
6.8%
5 2726
 
6.2%
4 2477
 
5.6%
6 2175
 
4.9%
1 1844
 
4.2%
2 1789
 
4.1%
8 1733
 
3.9%
3 1639
 
3.7%
Other values (9) 8200
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9785
22.2%
m 8780
19.9%
0 3003
 
6.8%
5 2726
 
6.2%
4 2477
 
5.6%
6 2175
 
4.9%
1 1844
 
4.2%
2 1789
 
4.1%
8 1733
 
3.9%
3 1639
 
3.7%
Other values (9) 8200
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9785
22.2%
m 8780
19.9%
0 3003
 
6.8%
5 2726
 
6.2%
4 2477
 
5.6%
6 2175
 
4.9%
1 1844
 
4.2%
2 1789
 
4.1%
8 1733
 
3.9%
3 1639
 
3.7%
Other values (9) 8200
18.6%

Bedrooms_df1
Categorical

IMBALANCE 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size609.4 KiB
No Bedrooms
3835 
2 PN
1720 
3 PN
1605 
4 PN
1438 
5 PN
509 
Other values (32)
678 

Length

Max length11
Median length4
Mean length6.7580991
Min length4

Characters and Unicode

Total characters66128
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row2 PN
2nd rowNo Bedrooms
3rd row5 PN
4th rowNo Bedrooms
5th rowNo Bedrooms

Common Values

ValueCountFrequency (%)
No Bedrooms 3835
39.2%
2 PN 1720
17.6%
3 PN 1605
16.4%
4 PN 1438
 
14.7%
5 PN 509
 
5.2%
6 PN 222
 
2.3%
1 PN 141
 
1.4%
7 PN 80
 
0.8%
8 PN 65
 
0.7%
9 PN 32
 
0.3%
Other values (27) 138
 
1.4%

Length

2024-06-21T15:42:30.759471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pn 5950
30.4%
no 3835
19.6%
bedrooms 3835
19.6%
2 1720
 
8.8%
3 1605
 
8.2%
4 1438
 
7.3%
5 509
 
2.6%
6 222
 
1.1%
1 141
 
0.7%
7 80
 
0.4%
Other values (29) 235
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 11505
17.4%
N 9785
14.8%
9785
14.8%
P 5950
9.0%
e 3835
 
5.8%
d 3835
 
5.8%
r 3835
 
5.8%
m 3835
 
5.8%
s 3835
 
5.8%
B 3835
 
5.8%
Other values (10) 6093
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 11505
17.4%
N 9785
14.8%
9785
14.8%
P 5950
9.0%
e 3835
 
5.8%
d 3835
 
5.8%
r 3835
 
5.8%
m 3835
 
5.8%
s 3835
 
5.8%
B 3835
 
5.8%
Other values (10) 6093
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 11505
17.4%
N 9785
14.8%
9785
14.8%
P 5950
9.0%
e 3835
 
5.8%
d 3835
 
5.8%
r 3835
 
5.8%
m 3835
 
5.8%
s 3835
 
5.8%
B 3835
 
5.8%
Other values (10) 6093
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 11505
17.4%
N 9785
14.8%
9785
14.8%
P 5950
9.0%
e 3835
 
5.8%
d 3835
 
5.8%
r 3835
 
5.8%
m 3835
 
5.8%
s 3835
 
5.8%
B 3835
 
5.8%
Other values (10) 6093
9.2%

Bathrooms_df1
Categorical

IMBALANCE 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size615.4 KiB
No Bathrooms
4134 
2 WC
1862 
3 WC
1396 
4 WC
955 
5 WC
559 
Other values (31)
879 

Length

Max length12
Median length4
Mean length7.3931528
Min length4

Characters and Unicode

Total characters72342
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row1 WC
2nd rowNo Bathrooms
3rd row6 WC
4th rowNo Bathrooms
5th rowNo Bathrooms

Common Values

ValueCountFrequency (%)
No Bathrooms 4134
42.2%
2 WC 1862
19.0%
3 WC 1396
 
14.3%
4 WC 955
 
9.8%
5 WC 559
 
5.7%
1 WC 329
 
3.4%
6 WC 242
 
2.5%
7 WC 99
 
1.0%
8 WC 68
 
0.7%
10 WC 30
 
0.3%
Other values (26) 111
 
1.1%

Length

2024-06-21T15:42:31.020357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wc 5651
28.9%
no 4134
21.1%
bathrooms 4134
21.1%
2 1862
 
9.5%
3 1396
 
7.1%
4 955
 
4.9%
5 559
 
2.9%
1 329
 
1.7%
6 242
 
1.2%
7 99
 
0.5%
Other values (28) 209
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o 12402
17.1%
9785
13.5%
W 5651
7.8%
C 5651
7.8%
r 4134
 
5.7%
s 4134
 
5.7%
m 4134
 
5.7%
N 4134
 
5.7%
h 4134
 
5.7%
t 4134
 
5.7%
Other values (12) 14049
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 12402
17.1%
9785
13.5%
W 5651
7.8%
C 5651
7.8%
r 4134
 
5.7%
s 4134
 
5.7%
m 4134
 
5.7%
N 4134
 
5.7%
h 4134
 
5.7%
t 4134
 
5.7%
Other values (12) 14049
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 12402
17.1%
9785
13.5%
W 5651
7.8%
C 5651
7.8%
r 4134
 
5.7%
s 4134
 
5.7%
m 4134
 
5.7%
N 4134
 
5.7%
h 4134
 
5.7%
t 4134
 
5.7%
Other values (12) 14049
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 12402
17.1%
9785
13.5%
W 5651
7.8%
C 5651
7.8%
r 4134
 
5.7%
s 4134
 
5.7%
m 4134
 
5.7%
N 4134
 
5.7%
h 4134
 
5.7%
t 4134
 
5.7%
Other values (12) 14049
19.4%
Distinct2560
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-06-21T15:42:31.625963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length255
Median length10
Mean length20.05161
Min length1

Characters and Unicode

Total characters196205
Distinct characters201
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1935 ?
Unique (%)19.8%

Sample

1st rowThành phố Hồ Chí Minh
2nd rowNo Address
3rd rowĐường Xóm Chiếu
4th rowNhà Chính chủ hẻm taxi Mễ Cốc, P15, Q8.
5th rowNo Address
ValueCountFrequency (%)
no 4772
 
11.3%
address 4772
 
11.3%
minh 1470
 
3.5%
quận 1457
 
3.4%
phường 1321
 
3.1%
bình 1321
 
3.1%
tân 1296
 
3.1%
chí 1240
 
2.9%
hồ 1235
 
2.9%
đường 952
 
2.3%
Other values (1301) 22450
53.1%
2024-06-21T15:42:32.483068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32601
 
16.6%
n 14709
 
7.5%
h 14106
 
7.2%
s 9685
 
4.9%
d 9574
 
4.9%
N 6475
 
3.3%
T 5962
 
3.0%
o 5825
 
3.0%
r 5536
 
2.8%
, 5330
 
2.7%
Other values (191) 86402
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32601
 
16.6%
n 14709
 
7.5%
h 14106
 
7.2%
s 9685
 
4.9%
d 9574
 
4.9%
N 6475
 
3.3%
T 5962
 
3.0%
o 5825
 
3.0%
r 5536
 
2.8%
, 5330
 
2.7%
Other values (191) 86402
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32601
 
16.6%
n 14709
 
7.5%
h 14106
 
7.2%
s 9685
 
4.9%
d 9574
 
4.9%
N 6475
 
3.3%
T 5962
 
3.0%
o 5825
 
3.0%
r 5536
 
2.8%
, 5330
 
2.7%
Other values (191) 86402
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32601
 
16.6%
n 14709
 
7.5%
h 14106
 
7.2%
s 9685
 
4.9%
d 9574
 
4.9%
N 6475
 
3.3%
T 5962
 
3.0%
o 5825
 
3.0%
r 5536
 
2.8%
, 5330
 
2.7%
Other values (191) 86402
44.0%

Listing ID
Real number (ℝ)

UNIQUE 

Distinct9785
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267877.37
Minimum250383
Maximum285164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.6 KiB
2024-06-21T15:42:32.663768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum250383
5-th percentile251918.4
Q1258923
median267513
Q3277078
95-th percentile283193.2
Maximum285164
Range34781
Interquartile range (IQR)18155

Descriptive statistics

Standard deviation10094.881
Coefficient of variation (CV)0.037684709
Kurtosis-1.2343745
Mean267877.37
Median Absolute Deviation (MAD)8996
Skewness-0.011984764
Sum2.6211801 × 109
Variance1.0190662 × 108
MonotonicityNot monotonic
2024-06-21T15:42:32.845733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285164 1
 
< 0.1%
257378 1
 
< 0.1%
257402 1
 
< 0.1%
257394 1
 
< 0.1%
257386 1
 
< 0.1%
257384 1
 
< 0.1%
257381 1
 
< 0.1%
257380 1
 
< 0.1%
257379 1
 
< 0.1%
257377 1
 
< 0.1%
Other values (9775) 9775
99.9%
ValueCountFrequency (%)
250383 1
< 0.1%
250385 1
< 0.1%
250394 1
< 0.1%
250397 1
< 0.1%
250400 1
< 0.1%
250406 1
< 0.1%
250407 1
< 0.1%
250408 1
< 0.1%
250411 1
< 0.1%
250413 1
< 0.1%
ValueCountFrequency (%)
285164 1
< 0.1%
285163 1
< 0.1%
285162 1
< 0.1%
285161 1
< 0.1%
285159 1
< 0.1%
285145 1
< 0.1%
285142 1
< 0.1%
285135 1
< 0.1%
285129 1
< 0.1%
285126 1
< 0.1%

Date
Date

Distinct9196
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size76.6 KiB
Minimum2022-09-06 07:16:00
Maximum2023-12-10 20:49:00
2024-06-21T15:42:33.023931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:33.196187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Price_df2
Text

MISSING 

Distinct2517
Distinct (%)26.1%
Missing144
Missing (%)1.5%
Memory size1.0 MiB
2024-06-21T15:42:33.843486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length179
Median length97
Mean length10.044601
Min length1

Characters and Unicode

Total characters96840
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1521 ?
Unique (%)15.8%

Sample

1st row12,8 tỷ VND
2nd row8,99 tỷ
3rd row3.4 tỷ VND
4th row7.39 tỷ VND
5th row16 tỷ VND
ValueCountFrequency (%)
tỷ 8588
30.5%
vnd 4935
17.5%
4 532
 
1.9%
3 496
 
1.8%
triệu 471
 
1.7%
not 459
 
1.6%
5 457
 
1.6%
the 366
 
1.3%
in 354
 
1.3%
6 343
 
1.2%
Other values (1398) 11136
39.6%
2024-06-21T15:42:34.805296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18496
19.1%
t 10957
 
11.3%
8623
 
8.9%
N 5417
 
5.6%
D 4962
 
5.1%
V 4951
 
5.1%
. 4027
 
4.2%
5 3805
 
3.9%
1 2241
 
2.3%
i 2205
 
2.3%
Other values (131) 31156
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18496
19.1%
t 10957
 
11.3%
8623
 
8.9%
N 5417
 
5.6%
D 4962
 
5.1%
V 4951
 
5.1%
. 4027
 
4.2%
5 3805
 
3.9%
1 2241
 
2.3%
i 2205
 
2.3%
Other values (131) 31156
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18496
19.1%
t 10957
 
11.3%
8623
 
8.9%
N 5417
 
5.6%
D 4962
 
5.1%
V 4951
 
5.1%
. 4027
 
4.2%
5 3805
 
3.9%
1 2241
 
2.3%
i 2205
 
2.3%
Other values (131) 31156
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18496
19.1%
t 10957
 
11.3%
8623
 
8.9%
N 5417
 
5.6%
D 4962
 
5.1%
V 4951
 
5.1%
. 4027
 
4.2%
5 3805
 
3.9%
1 2241
 
2.3%
i 2205
 
2.3%
Other values (131) 31156
32.2%

Area_df2
Text

MISSING 

Distinct4356
Distinct (%)45.2%
Missing151
Missing (%)1.5%
Memory size733.7 KiB
2024-06-21T15:42:35.446219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length147
Median length109
Mean length11.403674
Min length2

Characters and Unicode

Total characters109863
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3511 ?
Unique (%)36.4%

Sample

1st row75m2 (3,2m x 21m)
2nd row85m ngang 8m
3rd row105m
4th row53m2
5th row4x20m
ValueCountFrequency (%)
x 2235
 
9.0%
m2 1043
 
4.2%
not 752
 
3.0%
the 553
 
2.2%
in 527
 
2.1%
specified 438
 
1.8%
text 426
 
1.7%
4m 397
 
1.6%
ngang 368
 
1.5%
4 344
 
1.4%
Other values (2982) 17873
71.6%
2024-06-21T15:42:36.276286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15328
 
14.0%
m 12393
 
11.3%
2 10160
 
9.2%
1 5821
 
5.3%
4 4984
 
4.5%
x 4962
 
4.5%
5 4939
 
4.5%
0 3476
 
3.2%
t 3384
 
3.1%
n 3195
 
2.9%
Other values (131) 41221
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15328
 
14.0%
m 12393
 
11.3%
2 10160
 
9.2%
1 5821
 
5.3%
4 4984
 
4.5%
x 4962
 
4.5%
5 4939
 
4.5%
0 3476
 
3.2%
t 3384
 
3.1%
n 3195
 
2.9%
Other values (131) 41221
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15328
 
14.0%
m 12393
 
11.3%
2 10160
 
9.2%
1 5821
 
5.3%
4 4984
 
4.5%
x 4962
 
4.5%
5 4939
 
4.5%
0 3476
 
3.2%
t 3384
 
3.1%
n 3195
 
2.9%
Other values (131) 41221
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15328
 
14.0%
m 12393
 
11.3%
2 10160
 
9.2%
1 5821
 
5.3%
4 4984
 
4.5%
x 4962
 
4.5%
5 4939
 
4.5%
0 3476
 
3.2%
t 3384
 
3.1%
n 3195
 
2.9%
Other values (131) 41221
37.5%

Bedrooms_df2
Text

MISSING 

Distinct259
Distinct (%)2.7%
Missing149
Missing (%)1.5%
Memory size607.5 KiB
2024-06-21T15:42:36.714526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length149
Median length1
Mean length5.205687
Min length1

Characters and Unicode

Total characters50162
Distinct characters104
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)1.9%

Sample

1st row5
2nd row7
3rd row4
4th row4
5th rowNot mentioned
ValueCountFrequency (%)
not 2578
18.2%
2 1998
14.1%
3 1895
13.4%
4 1703
12.0%
specified 1359
9.6%
mentioned 1115
7.9%
5 586
 
4.1%
6 278
 
2.0%
the 219
 
1.5%
in 204
 
1.4%
Other values (250) 2211
15.6%
2024-06-21T15:42:37.419743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5571
 
11.1%
t 4559
 
9.1%
4510
 
9.0%
i 4419
 
8.8%
o 4006
 
8.0%
n 3219
 
6.4%
d 2746
 
5.5%
N 2628
 
5.2%
2 2073
 
4.1%
3 1933
 
3.9%
Other values (94) 14498
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5571
 
11.1%
t 4559
 
9.1%
4510
 
9.0%
i 4419
 
8.8%
o 4006
 
8.0%
n 3219
 
6.4%
d 2746
 
5.5%
N 2628
 
5.2%
2 2073
 
4.1%
3 1933
 
3.9%
Other values (94) 14498
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5571
 
11.1%
t 4559
 
9.1%
4510
 
9.0%
i 4419
 
8.8%
o 4006
 
8.0%
n 3219
 
6.4%
d 2746
 
5.5%
N 2628
 
5.2%
2 2073
 
4.1%
3 1933
 
3.9%
Other values (94) 14498
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5571
 
11.1%
t 4559
 
9.1%
4510
 
9.0%
i 4419
 
8.8%
o 4006
 
8.0%
n 3219
 
6.4%
d 2746
 
5.5%
N 2628
 
5.2%
2 2073
 
4.1%
3 1933
 
3.9%
Other values (94) 14498
28.9%

Bathrooms_df2
Text

MISSING 

Distinct198
Distinct (%)2.1%
Missing151
Missing (%)1.5%
Memory size622.7 KiB
2024-06-21T15:42:37.780359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length94
Median length1
Mean length7.3130579
Min length1

Characters and Unicode

Total characters70454
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)1.3%

Sample

1st row6
2nd row3
3rd row3
4th row5
5th rowNot mentioned
ValueCountFrequency (%)
not 4147
25.8%
specified 2238
13.9%
2 1915
11.9%
mentioned 1775
11.0%
3 1356
 
8.4%
4 793
 
4.9%
5 528
 
3.3%
the 406
 
2.5%
in 387
 
2.4%
1 354
 
2.2%
Other values (194) 2168
13.5%
2024-06-21T15:42:38.370261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9007
12.8%
t 7392
10.5%
i 7207
10.2%
6433
9.1%
o 6251
 
8.9%
n 4597
 
6.5%
d 4321
 
6.1%
N 4124
 
5.9%
c 2549
 
3.6%
p 2548
 
3.6%
Other values (86) 16025
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9007
12.8%
t 7392
10.5%
i 7207
10.2%
6433
9.1%
o 6251
 
8.9%
n 4597
 
6.5%
d 4321
 
6.1%
N 4124
 
5.9%
c 2549
 
3.6%
p 2548
 
3.6%
Other values (86) 16025
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9007
12.8%
t 7392
10.5%
i 7207
10.2%
6433
9.1%
o 6251
 
8.9%
n 4597
 
6.5%
d 4321
 
6.1%
N 4124
 
5.9%
c 2549
 
3.6%
p 2548
 
3.6%
Other values (86) 16025
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9007
12.8%
t 7392
10.5%
i 7207
10.2%
6433
9.1%
o 6251
 
8.9%
n 4597
 
6.5%
d 4321
 
6.1%
N 4124
 
5.9%
c 2549
 
3.6%
p 2548
 
3.6%
Other values (86) 16025
22.7%

Floors
Text

MISSING 

Distinct615
Distinct (%)6.4%
Missing155
Missing (%)1.6%
Memory size746.8 KiB
2024-06-21T15:42:38.725413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length127
Median length1
Mean length6.3073728
Min length1

Characters and Unicode

Total characters60740
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique425 ?
Unique (%)4.4%

Sample

1st row4
2nd row1 trệt, 1 lầu
3rd row2
4th row4
5th rowNot mentioned
ValueCountFrequency (%)
1 3403
17.2%
2 2958
15.0%
3 2071
10.5%
lầu 1941
9.8%
trệt 1759
8.9%
4 1319
 
6.7%
not 1180
 
6.0%
5 722
 
3.7%
mentioned 629
 
3.2%
specified 482
 
2.4%
Other values (219) 3269
16.6%
2024-06-21T15:42:39.300528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10104
16.6%
t 6276
 
10.3%
1 3470
 
5.7%
2 2970
 
4.9%
n 2904
 
4.8%
e 2619
 
4.3%
l 2581
 
4.2%
o 2575
 
4.2%
2422
 
4.0%
r 2320
 
3.8%
Other values (102) 22499
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10104
16.6%
t 6276
 
10.3%
1 3470
 
5.7%
2 2970
 
4.9%
n 2904
 
4.8%
e 2619
 
4.3%
l 2581
 
4.2%
o 2575
 
4.2%
2422
 
4.0%
r 2320
 
3.8%
Other values (102) 22499
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10104
16.6%
t 6276
 
10.3%
1 3470
 
5.7%
2 2970
 
4.9%
n 2904
 
4.8%
e 2619
 
4.3%
l 2581
 
4.2%
o 2575
 
4.2%
2422
 
4.0%
r 2320
 
3.8%
Other values (102) 22499
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10104
16.6%
t 6276
 
10.3%
1 3470
 
5.7%
2 2970
 
4.9%
n 2904
 
4.8%
e 2619
 
4.3%
l 2581
 
4.2%
o 2575
 
4.2%
2422
 
4.0%
r 2320
 
3.8%
Other values (102) 22499
37.0%

Amenities
Text

MISSING 

Distinct8053
Distinct (%)83.8%
Missing174
Missing (%)1.8%
Memory size2.7 MiB
2024-06-21T15:42:39.945046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length478
Median length324
Mean length67.111019
Min length3

Characters and Unicode

Total characters645004
Distinct characters212
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7545 ?
Unique (%)78.5%

Sample

1st rowSân Thượng, Bếp, Mặt bằng buôn bán
2nd rowkhu vực xây dựng nhà cao tầng, ô tô ngủ trong nhà
3rd rowSân thượng, Nhà xây dựng kiên cố, Hẻm taxi tận cửa
4th rowKhu vực an ninh, dân trí cao, gần mặt tiền
5th rowAn ninh cao, Gần công viên đi bộ, Đường 18m có vỉa hè, Hiếm nhà có đầy đủ công năng
ValueCountFrequency (%)
công 2500
 
1.9%
sân 2247
 
1.7%
gần 2104
 
1.6%
phòng 2089
 
1.5%
trường 2055
 
1.5%
chợ 1933
 
1.4%
nhà 1872
 
1.4%
tiện 1802
 
1.3%
học 1716
 
1.3%
xe 1608
 
1.2%
Other values (3725) 115012
85.2%
2024-06-21T15:42:41.086601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125335
19.4%
n 60672
 
9.4%
h 44189
 
6.9%
, 28867
 
4.5%
g 28334
 
4.4%
t 27948
 
4.3%
c 25243
 
3.9%
i 22901
 
3.6%
a 16588
 
2.6%
u 13618
 
2.1%
Other values (202) 251309
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 645004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
125335
19.4%
n 60672
 
9.4%
h 44189
 
6.9%
, 28867
 
4.5%
g 28334
 
4.4%
t 27948
 
4.3%
c 25243
 
3.9%
i 22901
 
3.6%
a 16588
 
2.6%
u 13618
 
2.1%
Other values (202) 251309
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 645004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
125335
19.4%
n 60672
 
9.4%
h 44189
 
6.9%
, 28867
 
4.5%
g 28334
 
4.4%
t 27948
 
4.3%
c 25243
 
3.9%
i 22901
 
3.6%
a 16588
 
2.6%
u 13618
 
2.1%
Other values (202) 251309
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 645004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
125335
19.4%
n 60672
 
9.4%
h 44189
 
6.9%
, 28867
 
4.5%
g 28334
 
4.4%
t 27948
 
4.3%
c 25243
 
3.9%
i 22901
 
3.6%
a 16588
 
2.6%
u 13618
 
2.1%
Other values (202) 251309
39.0%

Street name
Text

MISSING 

Distinct2028
Distinct (%)21.1%
Missing152
Missing (%)1.6%
Memory size1.1 MiB
2024-06-21T15:42:41.775341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length137
Median length94
Mean length13.505346
Min length2

Characters and Unicode

Total characters130097
Distinct characters192
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1255 ?
Unique (%)13.0%

Sample

1st rowNguyễn Tất Thành
2nd rowHòa Bình
3rd rowMễ Cốc
4th rowPhan Huy Ích
5th rowBùi Tá Hán
ValueCountFrequency (%)
văn 1699
 
5.9%
not 1494
 
5.2%
nguyễn 1079
 
3.7%
đường 986
 
3.4%
881
 
3.1%
mentioned 757
 
2.6%
số 620
 
2.2%
specified 592
 
2.1%
phan 422
 
1.5%
quang 378
 
1.3%
Other values (1069) 19918
69.1%
2024-06-21T15:42:42.615886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19193
 
14.8%
n 14391
 
11.1%
h 6453
 
5.0%
g 6252
 
4.8%
i 5506
 
4.2%
t 4106
 
3.2%
T 3985
 
3.1%
u 3985
 
3.1%
o 3744
 
2.9%
e 3693
 
2.8%
Other values (182) 58789
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19193
 
14.8%
n 14391
 
11.1%
h 6453
 
5.0%
g 6252
 
4.8%
i 5506
 
4.2%
t 4106
 
3.2%
T 3985
 
3.1%
u 3985
 
3.1%
o 3744
 
2.9%
e 3693
 
2.8%
Other values (182) 58789
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19193
 
14.8%
n 14391
 
11.1%
h 6453
 
5.0%
g 6252
 
4.8%
i 5506
 
4.2%
t 4106
 
3.2%
T 3985
 
3.1%
u 3985
 
3.1%
o 3744
 
2.9%
e 3693
 
2.8%
Other values (182) 58789
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19193
 
14.8%
n 14391
 
11.1%
h 6453
 
5.0%
g 6252
 
4.8%
i 5506
 
4.2%
t 4106
 
3.2%
T 3985
 
3.1%
u 3985
 
3.1%
o 3744
 
2.9%
e 3693
 
2.8%
Other values (182) 58789
45.2%

Ward name
Text

MISSING 

Distinct733
Distinct (%)7.6%
Missing170
Missing (%)1.7%
Memory size897.8 KiB
2024-06-21T15:42:43.242797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length75
Median length68
Mean length11.949766
Min length1

Characters and Unicode

Total characters114897
Distinct characters148
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique381 ?
Unique (%)4.0%

Sample

1st rowNot mentioned
2nd rowTân Phú
3rd rowP15
4th rowNot specified
5th rowAn Phú
ValueCountFrequency (%)
not 3893
17.0%
phường 2479
 
10.8%
mentioned 1949
 
8.5%
specified 1678
 
7.3%
bình 779
 
3.4%
tân 706
 
3.1%
in 601
 
2.6%
the 600
 
2.6%
phú 498
 
2.2%
text 463
 
2.0%
Other values (407) 9262
40.4%
2024-06-21T15:42:44.164301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13293
 
11.6%
n 11677
 
10.2%
e 8731
 
7.6%
t 7876
 
6.9%
i 7576
 
6.6%
h 6536
 
5.7%
o 6465
 
5.6%
d 4186
 
3.6%
N 4100
 
3.6%
g 4051
 
3.5%
Other values (138) 40406
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13293
 
11.6%
n 11677
 
10.2%
e 8731
 
7.6%
t 7876
 
6.9%
i 7576
 
6.6%
h 6536
 
5.7%
o 6465
 
5.6%
d 4186
 
3.6%
N 4100
 
3.6%
g 4051
 
3.5%
Other values (138) 40406
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13293
 
11.6%
n 11677
 
10.2%
e 8731
 
7.6%
t 7876
 
6.9%
i 7576
 
6.6%
h 6536
 
5.7%
o 6465
 
5.6%
d 4186
 
3.6%
N 4100
 
3.6%
g 4051
 
3.5%
Other values (138) 40406
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13293
 
11.6%
n 11677
 
10.2%
e 8731
 
7.6%
t 7876
 
6.9%
i 7576
 
6.6%
h 6536
 
5.7%
o 6465
 
5.6%
d 4186
 
3.6%
N 4100
 
3.6%
g 4051
 
3.5%
Other values (138) 40406
35.2%

District name
Text

MISSING 

Distinct539
Distinct (%)5.6%
Missing149
Missing (%)1.5%
Memory size972.5 KiB
2024-06-21T15:42:44.739917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length94
Median length82
Mean length9.7567455
Min length1

Characters and Unicode

Total characters94016
Distinct characters147
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique364 ?
Unique (%)3.8%

Sample

1st rowQuận 4
2nd rowTân Phú
3rd rowQ8
4th rowTân Bình
5th rowQuận 2
ValueCountFrequency (%)
quận 3098
13.9%
bình 2529
 
11.4%
tân 2022
 
9.1%
not 1507
 
6.8%
1095
 
4.9%
vấp 1091
 
4.9%
phú 1052
 
4.7%
thạnh 992
 
4.5%
đức 731
 
3.3%
thủ 724
 
3.3%
Other values (364) 7401
33.3%
2024-06-21T15:42:45.503335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12606
 
13.4%
n 11858
 
12.6%
h 7703
 
8.2%
T 3982
 
4.2%
u 3800
 
4.0%
3618
 
3.8%
e 3552
 
3.8%
t 3480
 
3.7%
Q 3183
 
3.4%
i 3043
 
3.2%
Other values (137) 37191
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12606
 
13.4%
n 11858
 
12.6%
h 7703
 
8.2%
T 3982
 
4.2%
u 3800
 
4.0%
3618
 
3.8%
e 3552
 
3.8%
t 3480
 
3.7%
Q 3183
 
3.4%
i 3043
 
3.2%
Other values (137) 37191
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12606
 
13.4%
n 11858
 
12.6%
h 7703
 
8.2%
T 3982
 
4.2%
u 3800
 
4.0%
3618
 
3.8%
e 3552
 
3.8%
t 3480
 
3.7%
Q 3183
 
3.4%
i 3043
 
3.2%
Other values (137) 37191
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12606
 
13.4%
n 11858
 
12.6%
h 7703
 
8.2%
T 3982
 
4.2%
u 3800
 
4.0%
3618
 
3.8%
e 3552
 
3.8%
t 3480
 
3.7%
Q 3183
 
3.4%
i 3043
 
3.2%
Other values (137) 37191
39.6%

Frontages
Text

MISSING 

Distinct700
Distinct (%)7.3%
Missing175
Missing (%)1.8%
Memory size662.9 KiB
2024-06-21T15:42:46.106194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length101
Median length82
Mean length10.346722
Min length1

Characters and Unicode

Total characters99432
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique499 ?
Unique (%)5.2%

Sample

1st rowNot mentioned
2nd row2
3rd row37m2
4th rowNot specified
5th row4m
ValueCountFrequency (%)
not 5533
28.7%
mentioned 2808
14.6%
specified 2369
12.3%
the 781
 
4.1%
in 759
 
3.9%
text 555
 
2.9%
2 551
 
2.9%
1 542
 
2.8%
provided 334
 
1.7%
4m 328
 
1.7%
Other values (593) 4691
24.4%
2024-06-21T15:42:46.848148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12296
12.4%
t 11175
11.2%
9641
9.7%
i 9591
9.6%
o 8825
8.9%
n 7630
 
7.7%
d 5913
 
5.9%
N 5442
 
5.5%
m 5183
 
5.2%
p 2908
 
2.9%
Other values (121) 20828
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12296
12.4%
t 11175
11.2%
9641
9.7%
i 9591
9.6%
o 8825
8.9%
n 7630
 
7.7%
d 5913
 
5.9%
N 5442
 
5.5%
m 5183
 
5.2%
p 2908
 
2.9%
Other values (121) 20828
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12296
12.4%
t 11175
11.2%
9641
9.7%
i 9591
9.6%
o 8825
8.9%
n 7630
 
7.7%
d 5913
 
5.9%
N 5442
 
5.5%
m 5183
 
5.2%
p 2908
 
2.9%
Other values (121) 20828
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12296
12.4%
t 11175
11.2%
9641
9.7%
i 9591
9.6%
o 8825
8.9%
n 7630
 
7.7%
d 5913
 
5.9%
N 5442
 
5.5%
m 5183
 
5.2%
p 2908
 
2.9%
Other values (121) 20828
20.9%

Main road
Text

MISSING 

Distinct1166
Distinct (%)12.1%
Missing151
Missing (%)1.5%
Memory size1.0 MiB
2024-06-21T15:42:47.447722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length133
Median length127
Mean length13.401702
Min length3

Characters and Unicode

Total characters129112
Distinct characters154
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique980 ?
Unique (%)10.2%

Sample

1st rowMặt tiền ngay trục đường kinh doanh nhộn nhịp
2nd rowngay đường
3rd rowNhà trong hẻm
4th rowHẻm
5th rowNgoài lộ
ValueCountFrequency (%)
hẻm 5463
19.0%
nhà 3654
12.7%
trong 3244
 
11.3%
not 1825
 
6.3%
lộ 1482
 
5.1%
ngoài 1455
 
5.1%
mentioned 945
 
3.3%
specified 792
 
2.8%
xe 689
 
2.4%
hơi 526
 
1.8%
Other values (720) 8704
30.2%
2024-06-21T15:42:48.268238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19146
14.8%
n 9538
 
7.4%
h 9413
 
7.3%
t 9153
 
7.1%
o 8187
 
6.3%
m 7491
 
5.8%
i 6458
 
5.0%
N 6141
 
4.8%
g 6066
 
4.7%
e 5567
 
4.3%
Other values (144) 41952
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19146
14.8%
n 9538
 
7.4%
h 9413
 
7.3%
t 9153
 
7.1%
o 8187
 
6.3%
m 7491
 
5.8%
i 6458
 
5.0%
N 6141
 
4.8%
g 6066
 
4.7%
e 5567
 
4.3%
Other values (144) 41952
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19146
14.8%
n 9538
 
7.4%
h 9413
 
7.3%
t 9153
 
7.1%
o 8187
 
6.3%
m 7491
 
5.8%
i 6458
 
5.0%
N 6141
 
4.8%
g 6066
 
4.7%
e 5567
 
4.3%
Other values (144) 41952
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19146
14.8%
n 9538
 
7.4%
h 9413
 
7.3%
t 9153
 
7.1%
o 8187
 
6.3%
m 7491
 
5.8%
i 6458
 
5.0%
N 6141
 
4.8%
g 6066
 
4.7%
e 5567
 
4.3%
Other values (144) 41952
32.5%

Interactions

2024-06-21T15:42:27.070625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-21T15:42:27.301897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-21T15:42:27.633658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-21T15:42:27.939251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Price_df1Area_df1Bedrooms_df1Bathrooms_df1AddressListing IDDatePrice_df2Area_df2Bedrooms_df2Bathrooms_df2FloorsAmenitiesStreet nameWard nameDistrict nameFrontagesMain road
03899000000 tỷ150 m2 PN1 WCThành phố Hồ Chí Minh28516412/10/2023 17:1112,8 tỷ VND75m2 (3,2m x 21m)564Sân Thượng, Bếp, Mặt bằng buôn bánNguyễn Tất ThànhNot mentionedQuận 4Not mentionedMặt tiền ngay trục đường kinh doanh nhộn nhịp
1Thỏa thuậnNo AreaNo BedroomsNo BathroomsNo Address28512612/8/2023 11:538,99 tỷ85m ngang 8m731 trệt, 1 lầukhu vực xây dựng nhà cao tầng, ô tô ngủ trong nhàHòa BìnhTân PhúTân Phú2ngay đường
212.8 tỷ75 m5 PN6 WCĐường Xóm Chiếu28511812/8/2023 14:173.4 tỷ VND105m432Sân thượng, Nhà xây dựng kiên cố, Hẻm taxi tận cửaMễ CốcP15Q837m2Nhà trong hẻm
33.4 tỷ110 mNo BedroomsNo BathroomsNhà Chính chủ hẻm taxi Mễ Cốc, P15, Q8.28510912/8/2023 14:567.39 tỷ VND53m2454Khu vực an ninh, dân trí cao, gần mặt tiềnPhan Huy ÍchNot specifiedTân BìnhNot specifiedHẻm
4Thỏa thuận12 mNo BedroomsNo BathroomsNo Address28510712/8/2023 15:3616 tỷ VND4x20mNot mentionedNot mentionedNot mentionedAn ninh cao, Gần công viên đi bộ, Đường 18m có vỉa hè, Hiếm nhà có đầy đủ công năngBùi Tá HánAn PhúQuận 24mNgoài lộ
58.99 tỷ85 m7 PN3 WCNo Address28510212/8/2023 16:521.x tỷ VND12m2, diện tích ngang: 3m; dài 4m, diện tích sử dụng: 24m21Not specified2 (1 trệt, 1 lầu)Khu dân cư hiện hữu, không quy hoạchTrần Xuân SoạnTân HưngQuận 7Not specifiedHẻm xe máy
67.39 tỷ53 m4 PN4 WCNo Address28510012/8/2023 17:0823,5 tỷ118m2Not specifiedNot specifiedNot specifiedVí trí đẹp đối diện Trung tâm thương mại GigaMall, Vị trí khu vực kinh doanh sầm uất, tấp nập, Thích hợp xây cao ốc, văn phòng, khách sạn, showroom hoặc kinh doanh buôn bán, Hiện đang cho thuê 30tr/tháng, Hỗ trợ vay Ngân HàngPhạm Văn ĐồngHiệp Bình ChánhThủ Đức2Not specified
716 tỷ80 m6 PN6 WCQuận 2, Hồ Chí Minh28509812/8/2023 17:564 tỷ50m23Not mentionedNot mentionedGiao thông thuận tiện, dịch vụ tiện ích đầy đủ, gần trường học các cấp, gần chợ, đại họcNguyễn KhuyếnPhường 12Bình ThạnhNot mentionedHẻm
84.75 tỷ50 mNo BedroomsNo BathroomsNguyễn Khuyến, phường 12, quận Bình Thạnh28509712/8/2023 18:001.1 tỷ VND150m2 (5 x 30m)21Not mentionedSân sau trồng câyNot mentionedNot mentionedHuyện Nhà BèNot mentionedHẻm nhưng xe hơi tránh được
95.5 tỷNo AreaNo BedroomsNo BathroomsNo Address28508712/9/2023 9:315 tỷ VND96m2 (4.3m x 22.5m)Not specifiedNot specified1 trệt, 1 lửngNot specifiedNot specifiedBình TânTân PhúNot specifiedHẻm
Price_df1Area_df1Bedrooms_df1Bathrooms_df1AddressListing IDDatePrice_df2Area_df2Bedrooms_df2Bathrooms_df2FloorsAmenitiesStreet nameWard nameDistrict nameFrontagesMain road
97755.5 tỷ80 m3 PNNo BathroomsThành phố Hồ Chí Minh2786819/21/2023 11:18Giá không được cung cấp trong nội dung40m2, 3.9 x 10.5341 trệt, 2 lầuPhòng khách, bếp, gần chợ Phú Lâm, siêu thị, trường học, bến xe, bệnh việnKhông được cung cấpTân Hòa ĐôngQuận 6Không được cung cấpNgoài lộ
97764.1 tỷ33.3 mNo Bedrooms2 WCCách Mạng tháng 8, Phường 15, Quận 102786739/21/2023 13:42NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
97774 tỷ110 m5 PN5 WCĐình Phong Phú, Tăng Nhơn Phú B, Q.92787199/21/2023 21:274 tỷ110m255Not mentioned2 ph ra trường mầm non, UBND, siêu thị winmart, Giao thông thuận tiện kết nối Trung tâm Thủ Đức, Cao tốc Long Thành , Q.2, Q.1Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
97784.1 tỷ33.3 mNo Bedrooms2 WCCách Mạng tháng 8, Phường 15, Quận 102786729/21/2023 13:424 tỷ 133m2, 5.6x6222Nhà mới đẹp, trước chủ cho thuê 10tr/tháng, Sổ hồng có sẵn, Hỗ trợ pháp lý & Ngân hàngCách Mạng Tháng 8Phường 15Quận 102Nhà trong chợ Hòa Hưng, cách mặt tiền đường Cách Mạng Tháng 8 10m
9779Thỏa thuậnNo AreaNo BedroomsNo BathroomsNo Address2787189/21/2023 21:425 tỷ 580m2 (4.4x18)Not specifiedNot specified3 tầngSân để xe rộng, ban công lớn, giếng trời, gần UBND quận Tân Phú, Siêu Thị, Trường Học, Bênhk Viện, Ngân HàngNguyễn SơnNot specifiedTân Phú6mNot specified
97805.2 tỷ40 m3 PN4 WCNo Address2787179/21/2023 22:1114 tỷ VND (thương lượng) / 36 triệu VND (thuê)250m2532Thiết kế phù hợp với tiện ở và kinh doanhLê Văn LươngPhường Tân PhongQuận 7Not specifiedNgoài lộ
97817.8 tỷ89 m3 PN3 WCNo Address2787169/21/2023 22:166.68 tỷ VND380m24Not specified4Nội thất cao cấpTrương Đức ToànTân PhúQuận 114x80mHẻm ô tô
97826.68 tỷ80 m4 PN4 WCPhường Tân Tạo A, Quận Bình Tân, TP. HCM2787149/21/2023 22:46Giá không được cung cấp trong nội dung89m2, 6.3 x 14.5332 tầngPhòng khách, bếp, sân đậu ô tôĐường Kênh Tân HóaKhông được cung cấp trong nội dungQuận 6Không được cung cấp trong nội dungNgoài lộ
97834600000000 tỷ50 m4 PN3 WCThành phố Hồ Chí Minh2787139/22/2023 6:25Not specified in the listingNot specified in the listing4Not specified in the listing2Karaoke room, Steam room, Roof garden, Solar power system, Friendly neighborhood, Nearby schools and markets, GymHoàng Bật ĐạtPhường 15Quận Tân BìnhNot specified in the listingNot specified in the listing
97847.3 tỷ112 m4 PN4 WCHoàng Bật Đạt Tỷ Phường 15 Quận Tân Bình2787079/21/2023 8:374,6 tỷ VND50m2334BTCT, nội thất, sổ hồng riêng, hoàn công đầy đủ, pháp lý chuẩnNot mentioned in the listingNot mentioned in the listingTân Bình2Nhà trong hẻm